For this K24 midcareer investigator award in patient-oriented research (POR) award, my goals are: 1) to become an international leader in the primary prevention of type 2 diabetes and cardiovascular diseases by applying emerging digital technologies and social media; 2) to conduct high-impact research in testing innovative, cost-effective lifestyle interventions (physical activity) using machine leaning; and 3) to provide a hih quality mentorship and training environment that supports the conduct of high POR impact research in cardiovascular and diabetes prevention. This K24 award will allow me to take on a broader and more formal mentorship role not only in the School of Nursing but also throughout the University of California, San Francisco campus. The overall goals of the new research in this application are to develop decision rule algorithms and quantitative models to optimize real-time predictions of future physical activity intervention adherences and to pilot test these algorithms and models in an intervention trial. The specific aims are: 1) to determine the extent to which baseline factors can predict adherence in self-monitoring (using mobile apps and wearing advanced accelerometers); 2) to determine the extent to which early app-based self-monitoring and evaluation (e.g., advanced accelerometers and diary use, and step counts in the first few weeks) can be optimized to make real-time predictions of future treatment failures; 3) to assess the feasibility of the Physical Activity Plus intervention using developed real-time prediction models (developed in Aim 2), compared to a regular physical activity intervention, for changes in total daily steps and duration of moderate and intensive physical activity over a three-month period. To tailor intervention intensity, we will evaluate the frequency of study visits and real-time feedback to minimize treatment failures and maximize adherence. Junior investigators in the POR have adapted digital mobile technologies as part of their research tools and have generated large volumes of continuous data. However, only a few training opportunities are available in this research area; in particular, for methods to rigorously and fully utilize the richness of the data collected. Therefore, the proposed K24 application will also meet these mentees' urgent needs across disciplines.